OpenRaiser/Intern-Atlas huggingface dataset:
When Words Change the Model: Sensitivity of LLMs for Constraint Programming Modelling
Venue: arXiv cs.AI 2025
Year: 2025

One of the long-standing goals in optimisation and constraint programming is to describe a problem in natural language and automatically obtain an executable, efficient model. Large language models appear to bring this vision closer, showing impressive results in automatically generating models for classical benchmarks. However, much of this apparent success may derive from data contamination rather than genuine reasoning: many standard CP problems are likely included in the training data of these models. To examine this hypothesis, we systematically rephrased and perturbed a set of well-known CSPLib problems to preserve their structure while modifying their context and introducing misleading elements. We then compared the models produced by three representative LLMs across original and modified descriptions. Our qualitative analysis shows that while LLMs can produce syntactically valid and semantically plausible models, their performance drops sharply under contextual and linguistic variation, revealing shallow understanding and sensitivity to wording.
Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target mostly monocular perception. Therefore, the effectiveness of Physical Adversarial Examples (PAEs) on stereo-based binocular depth estimation remains largely unexplored. To this end, we propose the first texture-enabled physical adversarial attack against stereo matching models in the context of autonomous driving. Our method employs a 3D PAE with global camouflage texture rather than a local 2D patch-based one, ensuring both visual consistency and attack effectiveness across different viewpoints of stereo cameras. To cope with the disparity effect of these cameras, we also propose a new 3D stereo matching rendering module that allows the PAE to be aligned with real-world positions and headings in binocular vision. We further propose a novel merging attack that seamlessly blends the target into the environment through fine-grained PAE optimization. It has significantly enhanced stealth and lethality upon existing hiding attacks that fail to get seamlessly merged into the background. Extensive evaluations show that our PAEs can successfully fool the stereo models into producing erroneous depth information.
Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Large Language Models (LLMs) are increasingly used for decision-making and planning in autonomous driving, showing promising reasoning capabilities and potential to generalize across diverse traffic situations. However, current LLM-based driving agents lack explicit mechanisms to enforce traffic rules and often struggle to reliably detect small, safety-critical objects such as traffic lights and signs. To address this limitation, we introduce TLS-Assist, a modular redundancy layer that augments LLM-based autonomous driving agents with explicit traffic light and sign recognition. TLS-Assist converts detections into structured natural language messages that are injected into the LLM input, enforcing explicit attention to safety-critical cues. The framework is plug-and-play, model-agnostic, and supports both single-view and multi-view camera setups. We evaluate TLS-Assist in a closed-loop setup on the LangAuto benchmark in CARLA. The results demonstrate relative driving performance improvements of up to 14% over LMDrive and 7% over BEVDriver, while consistently reducing traffic light and sign infractions. We publicly release the code and models on https://github.com/iis-esslingen/TLS-Assist.
PIM or CXL-PIM? Understanding Architectural Trade-offs Through Large-Scale Benchmarking
Venue: arXiv cs.ET 2025
Year: 2025

Processing-in-memory (PIM) reduces data movement by executing near memory, but our large-scale characterization on real PIM hardware shows that end-to-end performance is often limited by disjoint host and device address spaces that force explicit staging transfers. In contrast, CXL-PIM provides a unified address space and cache-coherent access at the cost of higher access latency. These opposing interface models create workload-dependent tradeoffs that are not captured by small-scale studies. This work presents a side-by-side, large-scale comparison of PIM and CXL-PIM using measurements from real PIM hardware and trace-driven CXL modeling. We identify when unified-address access amortizes link latency enough to overcome transfer bottlenecks, and when tightly coupled PIM remains preferable. Our results reveal phase- and dataset-size regimes in which the relative ranking between the two architectures reverses, offering practical guidance for future near-memory system design.
Language as an Anchor: Preserving Relative Visual Geometry for Domain Incremental Learning
Venue: arXiv cs.CV 2025
Year: 2025

A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a fundamental dilemma. On one hand, projecting all domains into a single unified visual space leads to inter-domain interference and semantic distortion, as large shifts may vary with not only visual appearance but also underlying semantics. On the other hand, isolating domain-specific parameters causes knowledge fragmentation, creating "knowledge islands" that hamper knowledge reuse and exacerbate forgetting. To address this issue, we propose LAVA (Language-Anchored Visual Alignment), a novel DIL framework that replaces direct feature alignment with relative alignment driven by a text-based reference anchor. LAVA guides the visual representations of each incoming domain to preserve a consistent relative geometry, which is defined by mirroring the pairwise semantic similarities between the class names. This anchored geometric structure acts as a bridge across domains, enabling the retrieval of class-aware prior knowledge and facilitating robust feature aggregation. Extensive experiments on standard DIL benchmarks demonstrate that LAVA achieves significant performance improvements over state-of-the-arts. Code is available at https://github.com/ShuyiGeng/LAVA.
Context-aware, Ante-hoc Explanations of Driving Behaviour
Venue: arXiv cs.LO 2025
Year: 2025

Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs. Unfortunately, explaining the system behaviour of AI-based driving functions is particularly challenging, as decision-making processes are often opaque. The field of Explainability Engineering tackles this challenge by developing explanation models at design time. These models are designed from system design artefacts and stakeholder needs to develop correct and good explanations. To support this field, we propose an approach that enables context-aware, ante-hoc explanations of (un)expectable driving manoeuvres at runtime. The visual yet formal language Traffic Sequence Charts is used to formalise explanation contexts, as well as corresponding (un)expectable driving manoeuvres. A dedicated runtime monitoring enables context-recognition and ante-hoc presentation of explanations at runtime. In combination, we aim to support the bridging of correct and good explanations. Our method is demonstrated in a simulated overtaking.
Safe-ROS: An Architecture for Autonomous Robots in Safety-Critical Domains
Venue: arXiv cs.LO 2025
Year: 2025

Deploying autonomous robots in safety-critical domains requires architectures that ensure operational effectiveness and safety compliance. In this paper, we contribute the Safe-ROS architecture for developing reliable and verifiable autonomous robots in such domains. It features two distinct subsystems: (1) an intelligent control system that is responsible for normal/routine operations, and (2) a Safety System consisting of Safety Instrumented Functions (SIFs) that provide formally verifiable independent oversight. We demonstrate Safe-ROS on an AgileX Scout Mini robot performing autonomous inspection in a nuclear environment. One safety requirement is selected and instantiated as a SIF. To support verification, we implement the SIF as a cognitive agent, programmed to stop the robot whenever it detects that it is too close to an obstacle. We verify that the agent meets the safety requirement and integrate it into the autonomous inspection. This integration is also verified, and the full deployment is validated in a Gazebo simulation, and lab testing. We evaluate this architecture in the context of the UK nuclear sector, where safety and regulation are crucial aspects of deployment. Success criteria include the development of a formal property from the safety requirement, implementation, and verification of the SIF, and the integration of the SIF into the operational robotic autonomous system. Our results demonstrate that the Safe-ROS architecture can provide safety verifiable oversight while deploying autonomous robots in safety-critical domains, offering a robust framework that can be extended to additional requirements and various applications.
Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies
Venue: arXiv cs.RO 2025
Year: 2025

We propose a method for combining Harmonic Control Lyapunov-Barrier Functions (HCLBFs) derived from Signal Temporal Logic (STL) specifications with any given robot policy to turn an unsafe policy into a safe one with formal guarantees. The two components are combined via HCLBF-derived safety certificates, thus producing commands that preserve both safety and task-driven behavior. We demonstrate with a simple proof-of-concept implementation for an object-centric force-based policy trained through reinforcement learning for a movement task of a stationary robot arm that is able to avoid colliding with obstacles on a table top after combining the policy with the safety constraints. The proposed method can be generalized to more complex specifications and dynamic task settings.
The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys
Venue: arXiv cs.IT 2025
Year: 2025

This paper investigates the information-theoretic decentralized secure aggregation (DSA) problem under practical groupwise secret keys and collusion resilience. In DSA, $K$ users are interconnected through error-free broadcast channels. Each user holds a private input and aims to compute the sum of all other users' inputs, while satisfying the security constraint that no user, even when colluding with up to $T$ other users, can infer any information about the inputs beyond the recovered sum. To ensure security, users are equipped with secret keys to mask their inputs. Motivated by recent advances in efficient group-based key generation protocols, we consider the symmetric groupwise key setting, where every subset of $G$ users shares a group key that is independent of all other group keys. The problem is challenging because the recovery and security constraints must hold simultaneously for all users, and the structural constraints on the secret keys limit the flexibility of key correlations. We characterize the optimal rate region consisting of all achievable pairs of per-user broadcast communication rate and groupwise key rate. In particular, we show that DSA with groupwise keys is infeasible when $G=1$ or $G\ge K-T$. Otherwise, when $2\le G<K-T$, to securely compute one symbol of the desired sum, each user must broadcast at least one symbol, and each group key must contain at least $(K-T-2)/\binom{K-T-1}{G}$ independent symbols. Our results establish the fundamental limits of DSA with groupwise keys and provide design insights for communication- and key-efficient secure aggregation in decentralized learning systems.
From Topology to Behavioral Semantics: Enhancing BGP Security by Understanding BGP's Language with LLMs
Venue: arXiv cs.NI 2025
Year: 2025

The trust-based nature of Border Gateway Protocol (BGP) makes it vulnerable to disruptions like prefix hijacking and misconfigurations, threatening routing stability. Traditional detection relies on manual inspection with limited scalability. Machine/Deep Learning (M/DL) approaches automate detection but suffer from suboptimal precision, limited generalizability, and high retraining costs. This is because existing methods focus on topological structures rather than comprehensive semantic characteristics of Autonomous Systems (ASes), often misinterpreting functionally similar but topologically distant ASes. To address this, we propose BGPShield, an anomaly detection framework built on LLM embeddings that captures the Behavior Portrait and Routing Policy Rationale of each AS beyond topology, such as operational scale and global role. We propose a segment-wise aggregation scheme to transform AS descriptions into LLM representations without information loss, and a lightweight contrastive reduction network to compress them into a semantic-consistent version. Using these representations, our AR-DTW algorithm aligns and accumulates semantic distances to reveal behavioral inconsistencies. Evaluated on 16 real-world datasets, BGPShield detects 100% of verified anomalies with a false discovery rate below 5%. Notably, the employed LLMs were released prior to evaluation events, verifying generalizability. Furthermore, BGPShield constructs representations for unseen ASes within one second, significantly outperforming BEAM which demands costly retraining (averaging 65 hours).
Overview and Prospects of Using Integer Surrogate Keys for Data Warehouse Performance Optimization
Venue: arXiv cs.DB 2025
Year: 2025

The aim of this paper is to examine and demonstrate how integer-based datetime labels (integer surrogate keys for time) can optimize data-warehouse and time-series performance, proposing practical formats and algorithms and validating their efficiency on real-world workloads. It is shown that replacing standard DATE and TIMESTAMP types with 32- and 64-bit integer formats reduces storage requirements by 30-60 percent and speeds up query execution by 25-40 percent. The paper presents indexing, aggregation, compression, and batching algorithms demonstrating up to an eightfold increase in throughput. Practical examples from finance, telecommunications, IoT, and scientific research confirm the efficiency and versatility of the proposed approach.
A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Underwater image restoration and enhancement are crucial for correcting color distortion and restoring image details, thereby establishing a fundamental basis for subsequent underwater visual tasks. However, current deep learning methodologies in this area are frequently constrained by the scarcity of high-quality paired datasets. Since it is difficult to obtain pristine reference labels in underwater scenes, existing benchmarks often rely on manually selected results from enhancement algorithms, providing debatable reference images that lack globally consistent color and authentic supervision. This limits the model's capabilities in color restoration, image enhancement, and generalization. To overcome this limitation, we propose using in-air natural images as unambiguous reference targets and translating them into underwater-degraded versions, thereby constructing synthetic datasets that provide authentic supervision signals for model learning. Specifically, we establish a generative data framework based on unpaired image-to-image translation, producing a large-scale dataset that covers 6 representative underwater degradation types. The framework constructs synthetic datasets with precise ground-truth labels, which facilitate the learning of an accurate mapping from degraded underwater images to their pristine scene appearances. Extensive quantitative and qualitative experiments across 6 representative network architectures and 3 independent test sets show that models trained on our synthetic data achieve comparable or superior color restoration and generalization performance to those trained on existing benchmarks. This research provides a reliable and scalable data-driven solution for underwater image restoration and enhancement. The generated dataset is publicly available at: https://github.com/yftian2025/SynUIEDatasets.git.
Interaction-Aware 4D Gaussian Splatting for Dynamic Hand-Object Interaction Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

This paper focuses on a challenging setting of simultaneously modeling geometry and appearance of hand-object interaction scenes without any object priors. We follow the trend of dynamic 3D Gaussian Splatting based methods, and address several significant challenges. To model complex hand-object interaction with mutual occlusion and edge blur, we present interaction-aware hand-object Gaussians with newly introduced optimizable parameters aiming to adopt piecewise linear hypothesis for clearer structural representation. Moreover, considering the complementarity and tightness of hand shape and object shape during interaction dynamics, we incorporate hand information into object deformation field, constructing interaction-aware dynamic fields to model flexible motions. To further address difficulties in the optimization process, we propose a progressive strategy that handles dynamic regions and static background step by step. Correspondingly, explicit regularizations are designed to stabilize the hand-object representations for smooth motion transition, physical interaction reality, and coherent lighting. Experiments show that our approach surpasses existing dynamic 3D-GS-based methods and achieves state-of-the-art performance in reconstructing dynamic hand-object interaction.
Enhancing Cyber-Resilience in Cyber-Physical Systems of Systems:A Methodical Approach
Venue: arXiv cs.CE 2025
Year: 2025

Cyber-physical Systems of Systems (CPSoS) are becoming increasingly prevalent across sectors such as Industry 4.0 and smart homes, where they play a critical role in enabling intelligent, interconnected functionality. Addressing the challenges and resilience requirements of these complex environments, we propose a modified Cyber-Resilience Life-Cycle as a practical framework for sustainable risk mitigation. Our approach enhances the adaptability of CPSoS and supports resilience against evolving system complexities and potential disruptions. We conclude by outlining application scenarios for the modified life-cycle and highlighting its relevance in fostering cyber-resilience in operational systems.
ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection
Venue: arXiv cs.CV 2025
Year: 2025

Modern deepfakes evade detection by leaving subtle, domain-speci c artifacts that single branch networks miss. ForensicFlow addresses this by fusing evidence across three forensic dimensions: global visual inconsistencies (via ConvNeXt-tiny), ne-grained texture anomalies (via Swin Transformer-tiny), and spectral noise patterns (via CNN with channel attention). Our attention-based temporal pooling dynamically prioritizes high-evidence frames, while adaptive fusion weights each branch according to forgery type. Trained on CelebDF(v2) with Focal Loss, the model achieves AUC 0.9752, F1 0.9408, and accuracy 0.9208 out performing single-stream detectors. Ablation studies con rm branch synergy, and Grad-CAM visualizations validate focus on genuine manipulation regions (e.g., facial boundaries). This multi-domain fusion strategy establishes robustness against increasingly sophisticated forgeries.
CAPIRE: Modelling the Impact of Teacher Strikes and Inflation on Student Trajectories in Engineering Education
Venue: arXiv cs.CY 2025
Year: 2025

This study extends the CAPIRE framework with a macro-shock module to analyse the impact of teacher strikes and inflation on student trajectories in engineering education. Using data from 1,343 students across 15 cohorts (2004-2019) in a public engineering faculty in Argentina, we construct a leak-aware, multilevel feature set that incorporates national inflation indicators, lagged exposure to teacher strikes, and interaction terms between macro shocks and curriculum friction. Random Forest models with cohort-based validation demonstrate that macro features provide stable, non-trivial gains in early-semester dropout prediction (improvement in Macro F1 from 0.73 to 0.78), with inflation volatility at entry and a strike-weighted basic-cycle friction index amongst the most influential variables. Lag analysis reveals that strike exposure exerts its strongest association with dropout two to three semesters after the disruption (OR = 2.34), and that effects are concentrated in early, high-friction semesters. We then embed these empirical patterns into an agent-based model, defining scenarios for inflation-only, strikes-only, and combined crisis. Simulations reproduce three stylised facts: delayed strike effects, basic-cycle vulnerability, and non-linear amplification when inflation and strikes co-occur, with combined shocks generating dropout levels 18-23% higher than the sum of individual effects. We argue that teacher strikes and inflation operate as structurally mediated educational disruptors, acting through curriculum design and financial resilience rather than as isolated events. The framework contributes to multilevel dropout theory by demonstrating how macro-level shocks propagate through institutional structures to shape individual trajectories and provides empirically grounded tools for scenario planning in macroeconomically unstable contexts.
FHIRconnect: Towards a seamless integration of openEHR and FHIR
Venue: arXiv cs.SE 2025
Year: 2025

Healthcare interoperability between openEHR and HL7 FHIR remains challenging due to fundamental differences in their data modeling approaches and the absence of standardized transformation mechanisms. This paper presents FHIRconnect, a novel domain-specific language and open-source transformation engine that enables standardized, bidirectional data exchange between openEHR and FHIR. Our approach addresses critical interoperability gaps through a triple-layered architecture that achieves 65% mapping reuse across projects by leveraging international archetype-based foundations while supporting local customizations. Using this framework, FHIRconnect successfully mapped 24 international archetypes to 15 FHIR profiles across seven clinical domains. Key contributions include the first comprehensive DSL for openEHR-FHIR transformation with a formal specification, an open-source execution engine (openFHIR), and an accessible mapping library covering high-impact clinical archetypes. Together, these components establish the technical basis for community-driven mapping standardization, reducing reliance on custom ETL solutions and advancing syntactic and semantic interoperability in healthcare IT systems built on open standards.